Advances in Medical Image Processing, Segmentation and Classification
Medical data typically include physiological signals, diagnostic images, and treatment histories, offering essential insights into patient conditions and outcomes. Computer-aided diagnosis (CAD) systems—used for detection, segmentation, and classification—are now key components of clinical workflows...
Kaydedildi:
| Materyal Türü: | Online |
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| Dil: | İngilizce |
| Baskı/Yayın Bilgisi: |
MDPI - Multidisciplinary Digital Publishing Institute
2025
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| Konular: | |
| Online Erişim: | ONIX_20250812T110751_9783725841233_339 |
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| _version_ | 1869531082150379520 |
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| collection | Directory of Open Access Books |
| description | Medical data typically include physiological signals, diagnostic images, and treatment histories, offering essential insights into patient conditions and outcomes. Computer-aided diagnosis (CAD) systems—used for detection, segmentation, and classification—are now key components of clinical workflows. These systems apply image processing techniques to ensure accurate analysis across CT, MRI, X-ray, and ultrasound scans. Artificial intelligence (AI), especially machine learning and deep learning, has further advanced CAD by enabling automated, accurate disease detection. Yet, the success of such models depends on large, annotated datasets and expertise in preprocessing, modeling, and validation. AI-driven CAD systems have shown strong potential in diverse clinical settings. Future work should prioritize multi-center data sharing, federated learning, few-shot learning, and explainable AI to enhance reliability and adaptability. Integrating AI with technologies like the Internet of Medical Things (IoMT) opens doors to real-time, scalable diagnostics. With continued innovation and rigorous validation, AI is set to become an essential part of clinical decision-making. This volume presents cutting-edge research and strategies to address current gaps, aiming to improve patient outcomes and advance global healthcare systems. |
| format | Online |
| id | doab-20.500.12854ir-165584 |
| institution | Directory of Open Access Books |
| language | eng |
| publishDate | 2025 |
| publishDateRange | 2025 |
| publishDateSort | 2025 |
| publisher | MDPI - Multidisciplinary Digital Publishing Institute |
| publisherStr | MDPI - Multidisciplinary Digital Publishing Institute |
| record_format | ojs |
| spelling | doab-20.500.12854ir-1655842025-08-12T09:57:24Z Advances in Medical Image Processing, Segmentation and Classification Mustafa, Wan Azani Alquran, Hiam medical image/bio-signal analysis medical image segmentation/detection healthcare systems AI-based medical image registration medical image recognition biomedical systems diagnostic aid AI-based screening system medical image signal classification biomedical image retrieval medical image annotation biomedical image summarization/filtering cancer diagnosis machine learning deep learning artificial intelligence AI-based medical image diagnosis medical deep learning CAD systems XAI-based medical imaging patient/treatment stratification based on AI image processing synthetic medical image generation explainable AI in medicine thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine Medical data typically include physiological signals, diagnostic images, and treatment histories, offering essential insights into patient conditions and outcomes. Computer-aided diagnosis (CAD) systems—used for detection, segmentation, and classification—are now key components of clinical workflows. These systems apply image processing techniques to ensure accurate analysis across CT, MRI, X-ray, and ultrasound scans. Artificial intelligence (AI), especially machine learning and deep learning, has further advanced CAD by enabling automated, accurate disease detection. Yet, the success of such models depends on large, annotated datasets and expertise in preprocessing, modeling, and validation. AI-driven CAD systems have shown strong potential in diverse clinical settings. Future work should prioritize multi-center data sharing, federated learning, few-shot learning, and explainable AI to enhance reliability and adaptability. Integrating AI with technologies like the Internet of Medical Things (IoMT) opens doors to real-time, scalable diagnostics. With continued innovation and rigorous validation, AI is set to become an essential part of clinical decision-making. This volume presents cutting-edge research and strategies to address current gaps, aiming to improve patient outcomes and advance global healthcare systems. 2025-08-12T09:57:22Z 2025-08-12T09:57:22Z 2025 book ONIX_20250812T110751_9783725841233_339 9783725841233 9783725841240 https://directory.doabooks.org/handle/20.500.12854/165584 eng image/jpeg Attribution 4.0 International https://mdpi.com/books https://mdpi.com/books/pdfview/book/11088 MDPI - Multidisciplinary Digital Publishing Institute 10.3390/books978-3-7258-4124-0 10.3390/books978-3-7258-4124-0 46cabcaa-dd94-4bfe-87b4-55023c1b36d0 9783725841233 9783725841240 278 open access |
| spellingShingle | medical image/bio-signal analysis medical image segmentation/detection healthcare systems AI-based medical image registration medical image recognition biomedical systems diagnostic aid AI-based screening system medical image signal classification biomedical image retrieval medical image annotation biomedical image summarization/filtering cancer diagnosis machine learning deep learning artificial intelligence AI-based medical image diagnosis medical deep learning CAD systems XAI-based medical imaging patient/treatment stratification based on AI image processing synthetic medical image generation explainable AI in medicine thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine Advances in Medical Image Processing, Segmentation and Classification |
| title | Advances in Medical Image Processing, Segmentation and Classification |
| title_full | Advances in Medical Image Processing, Segmentation and Classification |
| title_fullStr | Advances in Medical Image Processing, Segmentation and Classification |
| title_full_unstemmed | Advances in Medical Image Processing, Segmentation and Classification |
| title_short | Advances in Medical Image Processing, Segmentation and Classification |
| title_sort | advances in medical image processing segmentation and classification |
| topic | medical image/bio-signal analysis medical image segmentation/detection healthcare systems AI-based medical image registration medical image recognition biomedical systems diagnostic aid AI-based screening system medical image signal classification biomedical image retrieval medical image annotation biomedical image summarization/filtering cancer diagnosis machine learning deep learning artificial intelligence AI-based medical image diagnosis medical deep learning CAD systems XAI-based medical imaging patient/treatment stratification based on AI image processing synthetic medical image generation explainable AI in medicine thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine |
| topic_facet | medical image/bio-signal analysis medical image segmentation/detection healthcare systems AI-based medical image registration medical image recognition biomedical systems diagnostic aid AI-based screening system medical image signal classification biomedical image retrieval medical image annotation biomedical image summarization/filtering cancer diagnosis machine learning deep learning artificial intelligence AI-based medical image diagnosis medical deep learning CAD systems XAI-based medical imaging patient/treatment stratification based on AI image processing synthetic medical image generation explainable AI in medicine thema EDItEUR::M Medicine and Nursing::MB Medicine: general issues::MBN Public health and preventive medicine |
| url | ONIX_20250812T110751_9783725841233_339 |